Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations580705
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory402.5 MiB
Average record size in memory726.8 B

Variable types

Text4
Categorical5
Numeric8

Alerts

emissions_t_CO2e is highly overall correlated with employees and 1 other fieldsHigh correlation
employees is highly overall correlated with emissions_t_CO2e and 1 other fieldsHigh correlation
industry_name is highly overall correlated with sector_codeHigh correlation
initiative_capex_GBP is highly overall correlated with initiative_financial_savings_GBPHigh correlation
initiative_categories is highly overall correlated with initiative_id and 1 other fieldsHigh correlation
initiative_financial_savings_GBP is highly overall correlated with initiative_capex_GBPHigh correlation
initiative_id is highly overall correlated with initiative_categories and 3 other fieldsHigh correlation
initiative_implementation_difficulty is highly overall correlated with initiative_id and 2 other fieldsHigh correlation
initiative_name is highly overall correlated with initiative_categories and 3 other fieldsHigh correlation
initiative_operation_difficulty is highly overall correlated with initiative_id and 2 other fieldsHigh correlation
sector_code is highly overall correlated with industry_nameHigh correlation
turnover is highly overall correlated with emissions_t_CO2e and 1 other fieldsHigh correlation
turnover is highly skewed (γ1 = 70.00006879) Skewed
employees is highly skewed (γ1 = 35.94868897) Skewed
emissions_t_CO2e is highly skewed (γ1 = 65.80633861) Skewed
initiative_financial_savings_GBP is highly skewed (γ1 = 78.86209053) Skewed
initiative_capex_GBP is highly skewed (γ1 = 26.03918668) Skewed
initiative_carbon_savings_t_CO2e has 27627 (4.8%) zeros Zeros
initiative_financial_savings_GBP has 42724 (7.4%) zeros Zeros
initiative_capex_GBP has 257522 (44.3%) zeros Zeros

Reproduction

Analysis started2025-05-20 23:45:13.082127
Analysis finished2025-05-20 23:45:44.349555
Duration31.27 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct27905
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size44.9 MiB
2025-05-21T00:45:44.571456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters18582560
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)< 0.1%

Sample

1st row17ddb3e9830082f66734f5655c0e34a8
2nd row17ddb3e9830082f66734f5655c0e34a8
3rd row17ddb3e9830082f66734f5655c0e34a8
4th row17ddb3e9830082f66734f5655c0e34a8
5th row17ddb3e9830082f66734f5655c0e34a8
ValueCountFrequency (%)
ae0789c5693a5f3d0beb2174d9d9fc9d 33
 
< 0.1%
5ec02ef1e9f7a67cd28298f25e8b6b97 33
 
< 0.1%
79fddc6adf723bb6b11b78b4f02bd04f 33
 
< 0.1%
710d9aef5812784b5add4afe65dad58a 33
 
< 0.1%
9eb845e8210f6f0fc920151f4bac4ce2 33
 
< 0.1%
3e8716e3e74a1e31a45a8cc983aa6e4a 32
 
< 0.1%
0c9108b2b08358b2113ed5ba20698e44 32
 
< 0.1%
c5440bf9a2191ddbf31cd3482182ce8c 32
 
< 0.1%
c64c834104f3d6b62f45b4ab55767351 32
 
< 0.1%
89e4d69b8948374b5dc8640076430d2a 32
 
< 0.1%
Other values (27895) 580380
99.9%
2025-05-21T00:45:45.017644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 1172602
 
6.3%
8 1169827
 
6.3%
4 1169493
 
6.3%
6 1168710
 
6.3%
e 1164077
 
6.3%
a 1164030
 
6.3%
7 1163679
 
6.3%
3 1163068
 
6.3%
d 1161445
 
6.3%
f 1160607
 
6.2%
Other values (6) 6925022
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18582560
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 1172602
 
6.3%
8 1169827
 
6.3%
4 1169493
 
6.3%
6 1168710
 
6.3%
e 1164077
 
6.3%
a 1164030
 
6.3%
7 1163679
 
6.3%
3 1163068
 
6.3%
d 1161445
 
6.3%
f 1160607
 
6.2%
Other values (6) 6925022
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18582560
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 1172602
 
6.3%
8 1169827
 
6.3%
4 1169493
 
6.3%
6 1168710
 
6.3%
e 1164077
 
6.3%
a 1164030
 
6.3%
7 1163679
 
6.3%
3 1163068
 
6.3%
d 1161445
 
6.3%
f 1160607
 
6.2%
Other values (6) 6925022
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18582560
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 1172602
 
6.3%
8 1169827
 
6.3%
4 1169493
 
6.3%
6 1168710
 
6.3%
e 1164077
 
6.3%
a 1164030
 
6.3%
7 1163679
 
6.3%
3 1163068
 
6.3%
d 1161445
 
6.3%
f 1160607
 
6.2%
Other values (6) 6925022
37.3%

industry_name
Categorical

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.7 MiB
M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES
95621 
P: EDUCATION
82331 
C: MANUFACTURING
81704 
N: ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES
71213 
G: WHOLESALE AND RETAIL TRADE; REPAIR OF MOTOR VEHICLES AND MOTORCYCLES
48592 
Other values (15)
201244 

Length

Max length125
Median length58
Mean length37.060375
Min length12

Characters and Unicode

Total characters21521145
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES
2nd rowM: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES
3rd rowM: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES
4th rowM: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES
5th rowM: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES

Common Values

ValueCountFrequency (%)
M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES 95621
16.5%
P: EDUCATION 82331
14.2%
C: MANUFACTURING 81704
14.1%
N: ADMINISTRATIVE AND SUPPORT SERVICE ACTIVITIES 71213
12.3%
G: WHOLESALE AND RETAIL TRADE; REPAIR OF MOTOR VEHICLES AND MOTORCYCLES 48592
8.4%
Q: HUMAN HEALTH AND SOCIAL WORK ACTIVITIES 42309
7.3%
I: ACCOMMODATION AND FOOD SERVICE ACTIVITIES 40854
7.0%
J: INFORMATION AND COMMUNICATION 34000
 
5.9%
F: CONSTRUCTION 20971
 
3.6%
S: OTHER SERVICE ACTIVITIES 17007
 
2.9%
Other values (10) 46103
7.9%

Length

2025-05-21T00:45:45.192319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 531890
 
17.8%
activities 280704
 
9.4%
service 129074
 
4.3%
m 95621
 
3.2%
professional 95621
 
3.2%
technical 95621
 
3.2%
scientific 95621
 
3.2%
education 82331
 
2.8%
p 82331
 
2.8%
c 81704
 
2.7%
Other values (75) 1420256
47.5%

Most occurring characters

ValueCountFrequency (%)
2410069
11.2%
I 2323317
10.8%
A 1997407
 
9.3%
T 1702300
 
7.9%
E 1588821
 
7.4%
N 1576036
 
7.3%
C 1438578
 
6.7%
O 1181197
 
5.5%
S 1134158
 
5.3%
R 1001727
 
4.7%
Other values (19) 5167535
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21521145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2410069
11.2%
I 2323317
10.8%
A 1997407
 
9.3%
T 1702300
 
7.9%
E 1588821
 
7.4%
N 1576036
 
7.3%
C 1438578
 
6.7%
O 1181197
 
5.5%
S 1134158
 
5.3%
R 1001727
 
4.7%
Other values (19) 5167535
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21521145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2410069
11.2%
I 2323317
10.8%
A 1997407
 
9.3%
T 1702300
 
7.9%
E 1588821
 
7.4%
N 1576036
 
7.3%
C 1438578
 
6.7%
O 1181197
 
5.5%
S 1134158
 
5.3%
R 1001727
 
4.7%
Other values (19) 5167535
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21521145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2410069
11.2%
I 2323317
10.8%
A 1997407
 
9.3%
T 1702300
 
7.9%
E 1588821
 
7.4%
N 1576036
 
7.3%
C 1438578
 
6.7%
O 1181197
 
5.5%
S 1134158
 
5.3%
R 1001727
 
4.7%
Other values (19) 5167535
24.0%

sector_code
Real number (ℝ)

High correlation 

Distinct128
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.478527
Minimum1
Maximum692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-21T00:45:45.344707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q155
median74
Q385
95-th percentile255
Maximum692
Range691
Interquartile range (IQR)30

Descriptive statistics

Standard deviation108.8787
Coefficient of variation (CV)1.1403475
Kurtosis18.596033
Mean95.478527
Median Absolute Deviation (MAD)13
Skewness4.2346606
Sum55444858
Variance11854.572
MonotonicityNot monotonic
2025-05-21T00:45:45.494251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 82331
 
14.2%
70 56591
 
9.7%
82 43638
 
7.5%
46 23234
 
4.0%
55 21198
 
3.7%
86 20582
 
3.5%
56 19656
 
3.4%
62 18739
 
3.2%
74 16122
 
2.8%
87 15472
 
2.7%
Other values (118) 263142
45.3%
ValueCountFrequency (%)
1 1697
0.3%
2 134
 
< 0.1%
3 113
 
< 0.1%
5 33
 
< 0.1%
6 501
 
0.1%
7 97
 
< 0.1%
8 744
0.1%
9 562
 
0.1%
12 17
 
< 0.1%
13 1562
0.3%
ValueCountFrequency (%)
692 1149
 
0.2%
691 2208
0.4%
683 3684
0.6%
682 4615
0.8%
681 1300
 
0.2%
495 19
 
< 0.1%
494 5078
0.9%
493 1860
 
0.3%
492 64
 
< 0.1%
491 176
 
< 0.1%
Distinct128
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.1 MiB
2025-05-21T00:45:45.845015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length127
Median length75
Mean length41.527027
Min length8

Characters and Unicode

Total characters24114952
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther professional, scientific and technical activities
2nd rowOther professional, scientific and technical activities
3rd rowOther professional, scientific and technical activities
4th rowOther professional, scientific and technical activities
5th rowOther professional, scientific and technical activities
ValueCountFrequency (%)
activities 368212
 
12.3%
and 326659
 
10.9%
of 207005
 
6.9%
other 100374
 
3.4%
support 91778
 
3.1%
office 87276
 
2.9%
education 82331
 
2.8%
consultancy 76479
 
2.6%
manufacture 63713
 
2.1%
management 56954
 
1.9%
Other values (313) 1526269
51.1%
2025-05-21T00:45:46.372591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2406345
 
10.0%
i 2287022
 
9.5%
e 2254024
 
9.3%
t 2144842
 
8.9%
a 1932912
 
8.0%
c 1502766
 
6.2%
o 1413627
 
5.9%
n 1406927
 
5.8%
s 1388324
 
5.8%
r 1150760
 
4.8%
Other values (35) 6227403
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24114952
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2406345
 
10.0%
i 2287022
 
9.5%
e 2254024
 
9.3%
t 2144842
 
8.9%
a 1932912
 
8.0%
c 1502766
 
6.2%
o 1413627
 
5.9%
n 1406927
 
5.8%
s 1388324
 
5.8%
r 1150760
 
4.8%
Other values (35) 6227403
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24114952
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2406345
 
10.0%
i 2287022
 
9.5%
e 2254024
 
9.3%
t 2144842
 
8.9%
a 1932912
 
8.0%
c 1502766
 
6.2%
o 1413627
 
5.9%
n 1406927
 
5.8%
s 1388324
 
5.8%
r 1150760
 
4.8%
Other values (35) 6227403
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24114952
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2406345
 
10.0%
i 2287022
 
9.5%
e 2254024
 
9.3%
t 2144842
 
8.9%
a 1932912
 
8.0%
c 1502766
 
6.2%
o 1413627
 
5.9%
n 1406927
 
5.8%
s 1388324
 
5.8%
r 1150760
 
4.8%
Other values (35) 6227403
25.8%
Distinct360
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size33.8 MiB
2025-05-21T00:45:46.716737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6968460
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGB_E07000180
2nd rowGB_E07000180
3rd rowGB_E07000180
4th rowGB_E07000180
5th rowGB_E07000180
ValueCountFrequency (%)
gb_e09000033 30694
 
5.3%
gb_e09000001 28295
 
4.9%
gb_e09000007 13824
 
2.4%
gb_e09000028 9769
 
1.7%
gb_e08000025 9549
 
1.6%
gb_e08000035 8820
 
1.5%
gb_e09000019 8517
 
1.5%
gb_e08000003 7847
 
1.4%
gb_e09000030 6826
 
1.2%
gb_e06000060 5906
 
1.0%
Other values (350) 450658
77.6%
2025-05-21T00:45:47.218088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2945126
42.3%
G 580705
 
8.3%
_ 580705
 
8.3%
B 580705
 
8.3%
E 525448
 
7.5%
1 264273
 
3.8%
9 236884
 
3.4%
7 232152
 
3.3%
6 222727
 
3.2%
2 218312
 
3.1%
Other values (7) 581423
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6968460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2945126
42.3%
G 580705
 
8.3%
_ 580705
 
8.3%
B 580705
 
8.3%
E 525448
 
7.5%
1 264273
 
3.8%
9 236884
 
3.4%
7 232152
 
3.3%
6 222727
 
3.2%
2 218312
 
3.1%
Other values (7) 581423
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6968460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2945126
42.3%
G 580705
 
8.3%
_ 580705
 
8.3%
B 580705
 
8.3%
E 525448
 
7.5%
1 264273
 
3.8%
9 236884
 
3.4%
7 232152
 
3.3%
6 222727
 
3.2%
2 218312
 
3.1%
Other values (7) 581423
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6968460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2945126
42.3%
G 580705
 
8.3%
_ 580705
 
8.3%
B 580705
 
8.3%
E 525448
 
7.5%
1 264273
 
3.8%
9 236884
 
3.4%
7 232152
 
3.3%
6 222727
 
3.2%
2 218312
 
3.1%
Other values (7) 581423
 
8.3%
Distinct360
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size33.5 MiB
2025-05-21T00:45:47.613302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length36
Median length25
Mean length11.525603
Min length4

Characters and Unicode

Total characters6692975
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVale of White Horse
2nd rowVale of White Horse
3rd rowVale of White Horse
4th rowVale of White Horse
5th rowVale of White Horse
ValueCountFrequency (%)
city 52553
 
5.7%
of 48171
 
5.3%
and 43424
 
4.7%
westminster 30694
 
3.3%
london 28295
 
3.1%
north 22127
 
2.4%
east 17732
 
1.9%
west 16169
 
1.8%
camden 13824
 
1.5%
south 13059
 
1.4%
Other values (398) 630597
68.8%
2025-05-21T00:45:48.152965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 631323
 
9.4%
o 493098
 
7.4%
r 486815
 
7.3%
n 469290
 
7.0%
t 445630
 
6.7%
i 376326
 
5.6%
a 372787
 
5.6%
s 364372
 
5.4%
335940
 
5.0%
h 304093
 
4.5%
Other values (40) 2413301
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6692975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 631323
 
9.4%
o 493098
 
7.4%
r 486815
 
7.3%
n 469290
 
7.0%
t 445630
 
6.7%
i 376326
 
5.6%
a 372787
 
5.6%
s 364372
 
5.4%
335940
 
5.0%
h 304093
 
4.5%
Other values (40) 2413301
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6692975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 631323
 
9.4%
o 493098
 
7.4%
r 486815
 
7.3%
n 469290
 
7.0%
t 445630
 
6.7%
i 376326
 
5.6%
a 372787
 
5.6%
s 364372
 
5.4%
335940
 
5.0%
h 304093
 
4.5%
Other values (40) 2413301
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6692975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 631323
 
9.4%
o 493098
 
7.4%
r 486815
 
7.3%
n 469290
 
7.0%
t 445630
 
6.7%
i 376326
 
5.6%
a 372787
 
5.6%
s 364372
 
5.4%
335940
 
5.0%
h 304093
 
4.5%
Other values (40) 2413301
36.1%

turnover
Real number (ℝ)

High correlation  Skewed 

Distinct25697
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9791693 × 108
Minimum100
Maximum2.53676 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-21T00:45:48.328113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile4303000
Q111800000
median25459925
Q367805000
95-th percentile4.66511 × 108
Maximum2.53676 × 1011
Range2.53676 × 1011
Interquartile range (IQR)56005000

Descriptive statistics

Standard deviation2.4871591 × 109
Coefficient of variation (CV)12.566682
Kurtosis6305.5122
Mean1.9791693 × 108
Median Absolute Deviation (MAD)17360049
Skewness70.000069
Sum1.1493135 × 1014
Variance6.1859602 × 1018
MonotonicityNot monotonic
2025-05-21T00:45:48.486077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18274000 142
 
< 0.1%
9206000 139
 
< 0.1%
12425000 125
 
< 0.1%
10390000 120
 
< 0.1%
9300000 119
 
< 0.1%
38460000 115
 
< 0.1%
6706000 114
 
< 0.1%
28485000 111
 
< 0.1%
2913000000 108
 
< 0.1%
11485000 104
 
< 0.1%
Other values (25687) 579508
99.8%
ValueCountFrequency (%)
100 14
< 0.1%
256.7 4
 
< 0.1%
917 14
< 0.1%
1464 3
 
< 0.1%
1737 23
< 0.1%
1977 23
< 0.1%
3000 14
< 0.1%
3305 14
< 0.1%
5432 15
< 0.1%
5600 16
< 0.1%
ValueCountFrequency (%)
2.53676 × 101128
< 0.1%
1.67215 × 101130
< 0.1%
6.5762 × 101018
< 0.1%
5.3195 × 101028
< 0.1%
4.9145 × 101028
< 0.1%
4.7481 × 101017
< 0.1%
4.5152 × 101016
< 0.1%
4.2418 × 101029
< 0.1%
4.0178 × 101018
< 0.1%
3.5958 × 101018
< 0.1%

employees
Real number (ℝ)

High correlation  Skewed 

Distinct2950
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean993.71667
Minimum11
Maximum619000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-21T00:45:48.629826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile77
Q1117
median196
Q3417
95-th percentile2081
Maximum619000
Range618989
Interquartile range (IQR)300

Descriptive statistics

Standard deviation9550.76
Coefficient of variation (CV)9.6111501
Kurtosis1672.4734
Mean993.71667
Median Absolute Deviation (MAD)98
Skewness35.948689
Sum5.7705624 × 108
Variance91217017
MonotonicityNot monotonic
2025-05-21T00:45:48.772773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 4333
 
0.7%
89 4247
 
0.7%
88 4181
 
0.7%
90 4141
 
0.7%
86 4017
 
0.7%
87 3982
 
0.7%
95 3680
 
0.6%
104 3653
 
0.6%
91 3621
 
0.6%
93 3590
 
0.6%
Other values (2940) 541260
93.2%
ValueCountFrequency (%)
11 40
< 0.1%
12 69
< 0.1%
13 19
 
< 0.1%
14 21
 
< 0.1%
15 37
< 0.1%
20 50
< 0.1%
23 15
 
< 0.1%
24 33
< 0.1%
25 14
 
< 0.1%
26 54
< 0.1%
ValueCountFrequency (%)
619000 7
 
< 0.1%
568000 19
< 0.1%
562460 1
 
< 0.1%
554000 18
< 0.1%
483000 16
< 0.1%
401000 21
< 0.1%
398000 28
< 0.1%
389000 21
< 0.1%
318000 14
< 0.1%
252000 30
< 0.1%

emissions_t_CO2e
Real number (ℝ)

High correlation  Skewed 

Distinct27639
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9262.6656
Minimum2.6974028
Maximum17754379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-21T00:45:48.939172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.6974028
5-th percentile56.417019
Q1217.1933
median654.0202
Q31893.191
95-th percentile13816.238
Maximum17754379
Range17754377
Interquartile range (IQR)1675.9977

Descriptive statistics

Standard deviation176943.53
Coefficient of variation (CV)19.102874
Kurtosis5545.8856
Mean9262.6656
Median Absolute Deviation (MAD)528.86604
Skewness65.806339
Sum5.3788762 × 109
Variance3.1309013 × 1010
MonotonicityNot monotonic
2025-05-21T00:45:49.101037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192.65865 90
 
< 0.1%
1171.82455 90
 
< 0.1%
515.7168 78
 
< 0.1%
2237.2963 72
 
< 0.1%
1335.83205 69
 
< 0.1%
2697.54765 69
 
< 0.1%
554.356 62
 
< 0.1%
796.79665 61
 
< 0.1%
893.0443 61
 
< 0.1%
1359.63425 61
 
< 0.1%
Other values (27629) 579992
99.9%
ValueCountFrequency (%)
2.6974028 19
< 0.1%
3.69347715 16
< 0.1%
5.8020656 19
< 0.1%
5.91779275 16
< 0.1%
8.0556467 25
< 0.1%
8.28460685 14
< 0.1%
8.3449303 25
< 0.1%
8.8236568 14
< 0.1%
9.5772768 18
< 0.1%
9.9648512 24
< 0.1%
ValueCountFrequency (%)
17754379.26 17
< 0.1%
17541064.19 9
< 0.1%
13769821.79 6
 
< 0.1%
10369265.28 8
< 0.1%
9661355.2 17
< 0.1%
8710834.694 9
< 0.1%
8114309.193 10
< 0.1%
6568843.631 6
 
< 0.1%
6299193.586 9
< 0.1%
4994398.526 10
< 0.1%

initiative_id
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.468954
Minimum9
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-21T00:45:49.248674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12
Q139
median67
Q376
95-th percentile110
Maximum113
Range104
Interquartile range (IQR)37

Descriptive statistics

Standard deviation30.431452
Coefficient of variation (CV)0.48714522
Kurtosis-0.79889869
Mean62.468954
Median Absolute Deviation (MAD)25
Skewness-0.072881021
Sum36276034
Variance926.07328
MonotonicityNot monotonic
2025-05-21T00:45:49.407248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
70 27627
 
4.8%
69 26427
 
4.6%
68 26365
 
4.5%
67 26330
 
4.5%
113 25936
 
4.5%
58 21865
 
3.8%
62 20282
 
3.5%
76 19364
 
3.3%
74 18305
 
3.2%
9 16123
 
2.8%
Other values (39) 352081
60.6%
ValueCountFrequency (%)
9 16123
2.8%
11 12419
2.1%
12 13998
2.4%
13 13997
2.4%
16 13903
2.4%
17 13995
2.4%
21 13895
2.4%
36 15070
2.6%
37 11303
1.9%
38 11377
2.0%
ValueCountFrequency (%)
113 25936
4.5%
112 54
 
< 0.1%
111 72
 
< 0.1%
110 16034
2.8%
109 1107
 
0.2%
108 15653
2.7%
107 15726
2.7%
105 15638
2.7%
104 15640
2.7%
103 171
 
< 0.1%

initiative_name
Categorical

High correlation 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.1 MiB
Conduct an energy audit for ESOS compliance
 
27627
Regularly clean and change HVAC filters
 
26427
Insulate a boiler
 
26365
Install an economiser on an existing boiler
 
26330
Install optimiser to reduce grid voltage for electrical equipment
 
25936
Other values (44)
448020 

Length

Max length87
Median length57
Mean length45.036829
Min length17

Characters and Unicode

Total characters26153112
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConfigure power saving for electronics
2nd rowInstall optimiser to reduce grid voltage for electrical equipment
3rd rowReplace 50% of milk with dairy alternatives in office canteens
4th rowOptimise food production in office canteens to meet demand
5th rowPurchase food for office canteens in sustainable packaging

Common Values

ValueCountFrequency (%)
Conduct an energy audit for ESOS compliance 27627
 
4.8%
Regularly clean and change HVAC filters 26427
 
4.6%
Insulate a boiler 26365
 
4.5%
Install an economiser on an existing boiler 26330
 
4.5%
Install optimiser to reduce grid voltage for electrical equipment 25936
 
4.5%
Install a combined heat and power system 21865
 
3.8%
Install telematic devices to optimise vehicle movement 20282
 
3.5%
Use biodiesel in existing diesel fleets 19364
 
3.3%
Replace van tyres with eco-tyres 18305
 
3.2%
Replace light bulbs with LEDs 16123
 
2.8%
Other values (39) 352081
60.6%

Length

2025-05-21T00:45:49.566287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
install 197694
 
4.9%
for 163527
 
4.1%
in 154887
 
3.9%
to 143617
 
3.6%
office 125555
 
3.1%
with 117992
 
2.9%
replace 109328
 
2.7%
an 90595
 
2.3%
a 89150
 
2.2%
canteens 78691
 
2.0%
Other values (160) 2743522
68.3%

Most occurring characters

ValueCountFrequency (%)
3433853
13.1%
e 2924222
11.2%
t 1793169
 
6.9%
i 1748677
 
6.7%
a 1688294
 
6.5%
n 1602888
 
6.1%
o 1563555
 
6.0%
s 1561338
 
6.0%
l 1382381
 
5.3%
r 1209428
 
4.6%
Other values (39) 7245307
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26153112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3433853
13.1%
e 2924222
11.2%
t 1793169
 
6.9%
i 1748677
 
6.7%
a 1688294
 
6.5%
n 1602888
 
6.1%
o 1563555
 
6.0%
s 1561338
 
6.0%
l 1382381
 
5.3%
r 1209428
 
4.6%
Other values (39) 7245307
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26153112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3433853
13.1%
e 2924222
11.2%
t 1793169
 
6.9%
i 1748677
 
6.7%
a 1688294
 
6.5%
n 1602888
 
6.1%
o 1563555
 
6.0%
s 1561338
 
6.0%
l 1382381
 
5.3%
r 1209428
 
4.6%
Other values (39) 7245307
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26153112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3433853
13.1%
e 2924222
11.2%
t 1793169
 
6.9%
i 1748677
 
6.7%
a 1688294
 
6.5%
n 1602888
 
6.1%
o 1563555
 
6.0%
s 1561338
 
6.0%
l 1382381
 
5.3%
r 1209428
 
4.6%
Other values (39) 7245307
27.7%

initiative_categories
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.7 MiB
Fuel and energy conservation
261143 
Transportation sustainability
137843 
Waste reduction
71633 
Sustainable policies and practices
42655 
Travel sustainability
31347 
Other values (3)
36084 

Length

Max length59
Median length34
Mean length26.329877
Min length11

Characters and Unicode

Total characters15289891
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFuel and energy conservation
2nd rowFuel and energy conservation
3rd rowSustainable policies and practices
4th rowWaste reduction
5th rowWaste reduction

Common Values

ValueCountFrequency (%)
Fuel and energy conservation 261143
45.0%
Transportation sustainability 137843
23.7%
Waste reduction 71633
 
12.3%
Sustainable policies and practices 42655
 
7.3%
Travel sustainability 31347
 
5.4%
Disclosures 27627
 
4.8%
Infrastructure sustainability; Fuel and energy conservation 8286
 
1.4%
Supply chain sustainability 171
 
< 0.1%

Length

2025-05-21T00:45:49.709029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T00:45:50.011780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
and 312084
17.6%
fuel 269429
15.2%
energy 269429
15.2%
conservation 269429
15.2%
sustainability 177647
10.0%
transportation 137843
7.8%
waste 71633
 
4.0%
reduction 71633
 
4.0%
sustainable 42655
 
2.4%
policies 42655
 
2.4%
Other values (6) 110257
 
6.2%

Most occurring characters

ValueCountFrequency (%)
n 1696449
11.1%
a 1451895
9.5%
e 1416207
 
9.3%
i 1210264
 
7.9%
1193989
 
7.8%
t 1145557
 
7.5%
s 1053331
 
6.9%
r 1012664
 
6.6%
o 956459
 
6.3%
u 605734
 
4.0%
Other values (17) 3547342
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15289891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1696449
11.1%
a 1451895
9.5%
e 1416207
 
9.3%
i 1210264
 
7.9%
1193989
 
7.8%
t 1145557
 
7.5%
s 1053331
 
6.9%
r 1012664
 
6.6%
o 956459
 
6.3%
u 605734
 
4.0%
Other values (17) 3547342
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15289891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1696449
11.1%
a 1451895
9.5%
e 1416207
 
9.3%
i 1210264
 
7.9%
1193989
 
7.8%
t 1145557
 
7.5%
s 1053331
 
6.9%
r 1012664
 
6.6%
o 956459
 
6.3%
u 605734
 
4.0%
Other values (17) 3547342
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15289891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1696449
11.1%
a 1451895
9.5%
e 1416207
 
9.3%
i 1210264
 
7.9%
1193989
 
7.8%
t 1145557
 
7.5%
s 1053331
 
6.9%
r 1012664
 
6.6%
o 956459
 
6.3%
u 605734
 
4.0%
Other values (17) 3547342
23.2%

initiative_carbon_savings_t_CO2e
Real number (ℝ)

Zeros 

Distinct66357
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean485.02103
Minimum0
Maximum160344.6
Zeros27627
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-21T00:45:50.235741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.023295825
Q10.532875
median8.70744
Q3171.34
95-th percentile1401.7811
Maximum160344.6
Range160344.6
Interquartile range (IQR)170.80712

Descriptive statistics

Standard deviation1977.6209
Coefficient of variation (CV)4.0773921
Kurtosis302.6283
Mean485.02103
Median Absolute Deviation (MAD)8.6602012
Skewness11.03441
Sum2.8165414 × 108
Variance3910984.5
MonotonicityNot monotonic
2025-05-21T00:45:50.392540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27627
 
4.8%
0.51875 16663
 
2.9%
0.1242444 16123
 
2.8%
0.380625 16075
 
2.8%
0.989625 16045
 
2.8%
0.452974375 9298
 
1.6%
0.5175 9260
 
1.6%
2.699691037 8924
 
1.5%
966.9061132 8161
 
1.4%
0.023295825 7877
 
1.4%
Other values (66347) 444652
76.6%
ValueCountFrequency (%)
0 27627
4.8%
0.0045349206 1
 
< 0.1%
0.00491283065 1
 
< 0.1%
0.0090698412 1
 
< 0.1%
0.01095939145 1
 
< 0.1%
0.013977495 5
 
< 0.1%
0.01625013215 1
 
< 0.1%
0.0181396824 1
 
< 0.1%
0.01927341255 19
 
< 0.1%
0.0196513226 21
 
< 0.1%
ValueCountFrequency (%)
160344.5974 1
 
< 0.1%
109066.6544 7
< 0.1%
108631.302 1
 
< 0.1%
86428.33076 1
 
< 0.1%
86124.28234 1
 
< 0.1%
85332 1
 
< 0.1%
84285.89865 1
 
< 0.1%
83598.54031 1
 
< 0.1%
74994.95906 1
 
< 0.1%
70996.224 1
 
< 0.1%

initiative_financial_savings_GBP
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct63282
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34672.64
Minimum-31184615
Maximum1.0186138 × 108
Zeros42724
Zeros (%)7.4%
Negative74385
Negative (%)12.8%
Memory size4.4 MiB
2025-05-21T00:45:50.579821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-31184615
5-th percentile-29619.643
Q122.704825
median312.8125
Q312484.726
95-th percentile231507.41
Maximum1.0186138 × 108
Range1.3304599 × 108
Interquartile range (IQR)12462.021

Descriptive statistics

Standard deviation357697.2
Coefficient of variation (CV)10.316411
Kurtosis17405.437
Mean34672.64
Median Absolute Deviation (MAD)618.0625
Skewness78.862091
Sum2.0134576 × 1010
Variance1.2794729 × 1011
MonotonicityNot monotonic
2025-05-21T00:45:50.754524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42724
 
7.4%
223.7604133 21894
 
3.8%
22.37604133 16116
 
2.8%
67.51562002 15355
 
2.6%
175.540612 15295
 
2.6%
335.8 10929
 
1.9%
312.8125 8950
 
1.5%
182.325 8924
 
1.5%
536.25 8902
 
1.5%
68635.4717 7740
 
1.3%
Other values (63272) 423876
73.0%
ValueCountFrequency (%)
-31184615.38 3
 
< 0.1%
-12473846.15 11
 
< 0.1%
-6236923.077 58
< 0.1%
-3071952 1
 
< 0.1%
-2695000 4
 
< 0.1%
-2611159.2 1
 
< 0.1%
-2450000 11
 
< 0.1%
-2065198.113 70
< 0.1%
-1968584.906 12
 
< 0.1%
-1960794 1
 
< 0.1%
ValueCountFrequency (%)
101861376.7 1
< 0.1%
69761926.27 1
< 0.1%
54711777.69 1
< 0.1%
53543916.12 1
< 0.1%
47641703.55 1
< 0.1%
36157536.04 1
< 0.1%
34405862.4 1
< 0.1%
32915751.54 1
< 0.1%
28372118.48 1
< 0.1%
24648559.84 1
< 0.1%

initiative_capex_GBP
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct35710
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean373780.23
Minimum0
Maximum4.4342474 × 108
Zeros257522
Zeros (%)44.3%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-21T00:45:50.910854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2340
Q334000
95-th percentile1500000
Maximum4.4342474 × 108
Range4.4342474 × 108
Interquartile range (IQR)34000

Descriptive statistics

Standard deviation2815169.1
Coefficient of variation (CV)7.531616
Kurtosis1666.3779
Mean373780.23
Median Absolute Deviation (MAD)2340
Skewness26.039187
Sum2.1705605 × 1011
Variance7.9251772 × 1012
MonotonicityNot monotonic
2025-05-21T00:45:51.073817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 257522
44.3%
5000 27671
 
4.8%
7000 21747
 
3.7%
3000 20981
 
3.6%
2340 10101
 
1.7%
513.6986301 9298
 
1.6%
6421.232877 9260
 
1.6%
50000 8348
 
1.4%
34246.57534 7819
 
1.3%
128000 7369
 
1.3%
Other values (35700) 200589
34.5%
ValueCountFrequency (%)
0 257522
44.3%
87.6 1
 
< 0.1%
94.9 1
 
< 0.1%
175.2 1
 
< 0.1%
211.7 1
 
< 0.1%
308.2191781 5
 
< 0.1%
313.9 1
 
< 0.1%
372.3 13
 
< 0.1%
379.6 16
 
< 0.1%
386.9 17
 
< 0.1%
ValueCountFrequency (%)
443424743.8 1
< 0.1%
238172276.8 1
< 0.1%
233088321.2 1
< 0.1%
207394705.3 1
< 0.1%
161380868.9 1
< 0.1%
157401624.4 1
< 0.1%
143289430.9 1
< 0.1%
123510007.2 1
< 0.1%
107300546 1
< 0.1%
101218268.1 1
< 0.1%

initiative_implementation_difficulty
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.5 MiB
Moderate
163625 
Minor
146944 
Negligible
122556 
Minimal
77971 
Significant
57506 
Other values (2)
 
12103

Length

Max length11
Median length10
Mean length7.8878053
Min length5

Characters and Unicode

Total characters4580488
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegligible
2nd rowMinor
3rd rowNegligible
4th rowMinimal
5th rowNegligible

Common Values

ValueCountFrequency (%)
Moderate 163625
28.2%
Minor 146944
25.3%
Negligible 122556
21.1%
Minimal 77971
13.4%
Significant 57506
 
9.9%
Substantial 12031
 
2.1%
Extreme 72
 
< 0.1%

Length

2025-05-21T00:45:51.217135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T00:45:51.352179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
moderate 163625
28.2%
minor 146944
25.3%
negligible 122556
21.1%
minimal 77971
13.4%
significant 57506
 
9.9%
substantial 12031
 
2.1%
extreme 72
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 732547
16.0%
e 572506
12.5%
M 388540
8.5%
n 351958
7.7%
l 335114
7.3%
a 323164
7.1%
r 310641
6.8%
o 310569
6.8%
g 302618
6.6%
t 245265
 
5.4%
Other values (11) 707566
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4580488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 732547
16.0%
e 572506
12.5%
M 388540
8.5%
n 351958
7.7%
l 335114
7.3%
a 323164
7.1%
r 310641
6.8%
o 310569
6.8%
g 302618
6.6%
t 245265
 
5.4%
Other values (11) 707566
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4580488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 732547
16.0%
e 572506
12.5%
M 388540
8.5%
n 351958
7.7%
l 335114
7.3%
a 323164
7.1%
r 310641
6.8%
o 310569
6.8%
g 302618
6.6%
t 245265
 
5.4%
Other values (11) 707566
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4580488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 732547
16.0%
e 572506
12.5%
M 388540
8.5%
n 351958
7.7%
l 335114
7.3%
a 323164
7.1%
r 310641
6.8%
o 310569
6.8%
g 302618
6.6%
t 245265
 
5.4%
Other values (11) 707566
15.4%

initiative_operation_difficulty
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.8 MiB
Negligible
222175 
Moderate
141275 
Minor
104037 
Significant
54794 
Minimal
51731 
Other values (2)
 
6693

Length

Max length11
Median length10
Mean length8.4557925
Min length5

Characters and Unicode

Total characters4910321
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegligible
2nd rowNegligible
3rd rowNegligible
4th rowModerate
5th rowNegligible

Common Values

ValueCountFrequency (%)
Negligible 222175
38.3%
Moderate 141275
24.3%
Minor 104037
17.9%
Significant 54794
 
9.4%
Minimal 51731
 
8.9%
Substantial 6621
 
1.1%
Extreme 72
 
< 0.1%

Length

2025-05-21T00:45:51.503506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-21T00:45:51.631002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
negligible 222175
38.3%
moderate 141275
24.3%
minor 104037
17.9%
significant 54794
 
9.4%
minimal 51731
 
8.9%
substantial 6621
 
1.1%
extreme 72
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 822852
16.8%
e 727044
14.8%
l 502702
10.2%
g 499144
10.2%
M 297043
 
6.0%
n 271977
 
5.5%
a 261042
 
5.3%
r 245384
 
5.0%
o 245312
 
5.0%
b 228796
 
4.7%
Other values (11) 809025
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4910321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 822852
16.8%
e 727044
14.8%
l 502702
10.2%
g 499144
10.2%
M 297043
 
6.0%
n 271977
 
5.5%
a 261042
 
5.3%
r 245384
 
5.0%
o 245312
 
5.0%
b 228796
 
4.7%
Other values (11) 809025
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4910321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 822852
16.8%
e 727044
14.8%
l 502702
10.2%
g 499144
10.2%
M 297043
 
6.0%
n 271977
 
5.5%
a 261042
 
5.3%
r 245384
 
5.0%
o 245312
 
5.0%
b 228796
 
4.7%
Other values (11) 809025
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4910321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 822852
16.8%
e 727044
14.8%
l 502702
10.2%
g 499144
10.2%
M 297043
 
6.0%
n 271977
 
5.5%
a 261042
 
5.3%
r 245384
 
5.0%
o 245312
 
5.0%
b 228796
 
4.7%
Other values (11) 809025
16.5%

Interactions

2025-05-21T00:45:40.830379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:31.165184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:32.491270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:33.781173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:35.086114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:36.755780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:38.090007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:39.476914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:41.005563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:31.340693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:32.651309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:33.956033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:35.560555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:36.931928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:38.265628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:39.636400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:41.149229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:31.499259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:32.825462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:34.099206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:35.729917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:37.089600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:38.433809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:39.796135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:41.451597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:31.659030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:32.983585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:34.258388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:35.895528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:37.249189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:38.599117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:39.971183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:41.625776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:31.838870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:33.143275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:34.433707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:36.071219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:37.408293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:38.775132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:40.147249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:41.768126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:31.997932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:33.302263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:34.576032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:36.230530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:37.583073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:38.950671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:40.306791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:41.959548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:32.173108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:33.462201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:34.750836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:36.422133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:37.740978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:39.141394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:40.497582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:42.119543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:32.332101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:33.626400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:34.929413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:36.597415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:37.933428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:39.317107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-05-21T00:45:40.660467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-05-21T00:45:51.752561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
emissions_t_CO2eemployeesindustry_nameinitiative_capex_GBPinitiative_carbon_savings_t_CO2einitiative_categoriesinitiative_financial_savings_GBPinitiative_idinitiative_implementation_difficultyinitiative_nameinitiative_operation_difficultysector_codeturnover
emissions_t_CO2e1.0000.5060.1070.1890.2900.0070.2030.1210.0190.0220.0180.0450.514
employees0.5061.0000.0240.0340.1750.0030.103-0.0160.0030.0000.0020.0320.605
industry_name0.1070.0241.0000.0300.0280.2510.0370.2170.2150.2350.1660.6900.020
initiative_capex_GBP0.1890.0340.0301.0000.3590.0180.614-0.0670.0160.0970.032-0.0910.113
initiative_carbon_savings_t_CO2e0.2900.1750.0280.3591.0000.0350.3710.3280.0760.1830.097-0.1050.226
initiative_categories0.0070.0030.2510.0180.0351.0000.0080.5470.3891.0000.3740.1250.000
initiative_financial_savings_GBP0.2030.1030.0370.6140.3710.0081.0000.1900.4080.3540.408-0.0680.144
initiative_id0.121-0.0160.217-0.0670.3280.5470.1901.0000.6161.0000.537-0.1040.066
initiative_implementation_difficulty0.0190.0030.2150.0160.0760.3890.4080.6161.0001.0000.6480.1260.000
initiative_name0.0220.0000.2350.0970.1831.0000.3541.0001.0001.0001.0000.1990.011
initiative_operation_difficulty0.0180.0020.1660.0320.0970.3740.4080.5370.6481.0001.0000.0960.000
sector_code0.0450.0320.690-0.091-0.1050.125-0.068-0.1040.1260.1990.0961.000-0.214
turnover0.5140.6050.0200.1130.2260.0000.1440.0660.0000.0110.000-0.2141.000

Missing values

2025-05-21T00:45:42.420689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-21T00:45:43.152834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

company_idindustry_namesector_codesector_namegeography_idgeography_nameturnoveremployeesemissions_t_CO2einitiative_idinitiative_nameinitiative_categoriesinitiative_carbon_savings_t_CO2einitiative_financial_savings_GBPinitiative_capex_GBPinitiative_implementation_difficultyinitiative_operation_difficulty
017ddb3e9830082f66734f5655c0e34a8M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES74Other professional, scientific and technical activitiesGB_E07000180Vale of White Horse19489692.012464.14952212Configure power saving for electronicsFuel and energy conservation0.04686132.3609000.000000NegligibleNegligible
117ddb3e9830082f66734f5655c0e34a8M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES74Other professional, scientific and technical activitiesGB_E07000180Vale of White Horse19489692.012464.149522113Install optimiser to reduce grid voltage for electrical equipmentFuel and energy conservation12.3191277825.91517634067.912151MinorNegligible
217ddb3e9830082f66734f5655c0e34a8M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES74Other professional, scientific and technical activitiesGB_E07000180Vale of White Horse19489692.012464.149522105Replace 50% of milk with dairy alternatives in office canteensSustainable policies and practices5.859000-1674.0000000.000000NegligibleNegligible
317ddb3e9830082f66734f5655c0e34a8M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES74Other professional, scientific and technical activitiesGB_E07000180Vale of White Horse19489692.012464.149522110Optimise food production in office canteens to meet demandWaste reduction46.50000018748.8000000.000000MinimalModerate
417ddb3e9830082f66734f5655c0e34a8M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES74Other professional, scientific and technical activitiesGB_E07000180Vale of White Horse19489692.012464.149522108Purchase food for office canteens in sustainable packagingWaste reduction10.713600-1422.9000000.000000NegligibleNegligible
517ddb3e9830082f66734f5655c0e34a8M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES74Other professional, scientific and technical activitiesGB_E07000180Vale of White Horse19489692.012464.149522104Have a vegetarian default meal in office canteensSustainable policies and practices9.5418005022.0000000.000000ModerateMinor
617ddb3e9830082f66734f5655c0e34a8M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES74Other professional, scientific and technical activitiesGB_E07000180Vale of White Horse19489692.012464.1495229Replace light bulbs with LEDsFuel and energy conservation0.452974312.812500513.698630NegligibleNegligible
717ddb3e9830082f66734f5655c0e34a8M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES74Other professional, scientific and technical activitiesGB_E07000180Vale of White Horse19489692.012464.14952238Provide a public transport benefitSustainable policies and practices38.6880000.0000000.000000NegligibleNegligible
817ddb3e9830082f66734f5655c0e34a8M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES74Other professional, scientific and technical activitiesGB_E07000180Vale of White Horse19489692.012464.14952242Use recycled paper in officesWaste reduction2.534560-226.300000905.200000MinimalNegligible
917ddb3e9830082f66734f5655c0e34a8M: PROFESSIONAL AND SCIENTIFIC AND TECHNICAL ACTIVITIES74Other professional, scientific and technical activitiesGB_E07000180Vale of White Horse19489692.012464.14952267Install an economiser on an existing boilerFuel and energy conservation0.989625175.5406125000.000000MinorMinor
company_idindustry_namesector_codesector_namegeography_idgeography_nameturnoveremployeesemissions_t_CO2einitiative_idinitiative_nameinitiative_categoriesinitiative_carbon_savings_t_CO2einitiative_financial_savings_GBPinitiative_capex_GBPinitiative_implementation_difficultyinitiative_operation_difficulty
580695b9f27988ca183aae78ed5c4ce129842eP: EDUCATION85EducationGB_E09000020Kensington and Chelsea13108986.0143903.85048760Install battery capacity to store off-peak electricityFuel and energy conservation0.517500536.2500006421.232877ModerateSignificant
580696b9f27988ca183aae78ed5c4ce129842eP: EDUCATION85EducationGB_E09000020Kensington and Chelsea13108986.0143903.85048742Use recycled paper in officesWaste reduction2.902480-259.1500001036.600000MinimalNegligible
580697b9f27988ca183aae78ed5c4ce129842eP: EDUCATION85EducationGB_E09000020Kensington and Chelsea13108986.0143903.85048737Buy bikes to encourage staff to cycle to workTravel sustainability16.6140000.00000014910.000000MinimalMinimal
580698b9f27988ca183aae78ed5c4ce129842eP: EDUCATION85EducationGB_E09000020Kensington and Chelsea13108986.0143903.85048721Install solar energy systems for office powerFuel and energy conservation5.1768502718.83561634246.575342SignificantSignificant
580699b9f27988ca183aae78ed5c4ce129842eP: EDUCATION85EducationGB_E09000020Kensington and Chelsea13108986.0143903.8504879Replace light bulbs with LEDsFuel and energy conservation0.452974312.812500513.698630NegligibleNegligible
580700b9f27988ca183aae78ed5c4ce129842eP: EDUCATION85EducationGB_E09000020Kensington and Chelsea13108986.0143903.85048717Install motion sensors for lightingFuel and energy conservation0.02329616.0875000.000000MinimalNegligible
580701b9f27988ca183aae78ed5c4ce129842eP: EDUCATION85EducationGB_E09000020Kensington and Chelsea13108986.0143903.85048774Replace van tyres with eco-tyresTransportation sustainability1069.28440841181.283019128000.000000ModerateModerate
580702b9f27988ca183aae78ed5c4ce129842eP: EDUCATION85EducationGB_E09000020Kensington and Chelsea13108986.0143903.850487107Set optimal temperature of food storage in office canteensWaste reduction27.3172501725.3000000.000000NegligibleNegligible
580703b9f27988ca183aae78ed5c4ce129842eP: EDUCATION85EducationGB_E09000020Kensington and Chelsea13108986.0143903.85048769Regularly clean and change HVAC filtersFuel and energy conservation0.124244335.8000000.000000MinorNegligible
580704b9f27988ca183aae78ed5c4ce129842eP: EDUCATION85EducationGB_E09000020Kensington and Chelsea13108986.0143903.85048713Configure auto-off for office electronics when not in useFuel and energy conservation0.214653148.2338000.000000NegligibleNegligible